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INNOVATIVE LSGTIME MODEL FOR CRIME SPATIOTEMPORAL PREDICTION BASED ON MINDSPORE FRAMEWORK

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arXiv 2025年

作者： Qin, Zhenkai Wei, Baozhong Gao, Caifeng School of Computing and Information Guangxi Police College Nanning Guangxi China Network Security Research Center Guangxi Police College Nanning Guangxi China Big Data and Policing Technology Laboratory Guangxi Police College Nanning Guangxi China Institute of Software Chinese Academy of Sciences Beijing China

With the acceleration of urbanization, the spatiotemporal characteristics of criminal activities have become increasingly complex. Accurate prediction of crime distribution is crucial for optimizing the allocation of police resources and preventing crime. This paper proposes LGSTime, a crime spatiotemporal prediction model that integrates Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and the Multi-head Sparse Self-attention mechanism. LSTM and GRU capture long-term dependencies in crime time series, such as seasonality and periodicity, through their unique gating mechanisms. The Multi-head Sparse Self-attention mechanism, on the other hand, focuses on both temporal and spatial features of criminal events simultaneously through parallel processing and sparsification techniques, significantly improving computational efficiency and prediction accuracy. The integrated model leverages the strengths of each technique to better handle complex spatiotemporal data. Experimental findings demonstrate that the model attains optimal performance across four real - world crime datasets. In comparison to the CNN model, it exhibits performance enhancements of 2.8%, 1.9%, and 1.4% in the Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) metrics respectively. These results offer a valuable reference for tackling the challenges in crime prediction. © 2025, CC0.

关键词： Deep learning

Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

A new classification method of SAR image based on OGMRF-RC model 5

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A new classification method of SAR image based on OGMRF-RC m...

IET International Radar Conference 2020, IET IRC 2020

作者： Li, Yuan Guo, Zhishun Wu, Lin Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475000 China College of Environment and Planning Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

Synthetic Aperture Radar (SAR) image classification is a key technique and research focus in the application of remote sensing. This paper proposes a new classification method of SAR image based on Object-based Gaussian-Markov Random Field model with Region Coefficients (OGMRF-RC), the new method use Fuzzy Probability (FP) represents the region's category probability, FP is obtained by regional posterior probability and edge information of neighborhood. Use FP to replace the unique class label of the region, which avoids the parameters estimation inaccurate of the feature field caused by the unique initial class label. And the parameter calculation method of feature field takes into account the feature information of all regions in the image. In this paper, we take the area around Baliwan in Kaifeng as study area, Sentinel-1 is used as the remote sensing data sources. FCM, K-means, classic MRF and OGMRF-RC are used as contrast methods, the experimental result demonstrates the accuracy of our method is better than these methods. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： Synthetic aperture radar

Spatio-temporal multi-step prediction of influenza outbreaks

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arXiv 2021年

作者： Zhang, Jie Nawata, Kazumitsu Wu, Hongyan Department of Technology Management for Innovation The University of Tokyo HongoTokyo1138656 Japan Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen University Town 1068 Xueyuan Avenue Shenzhen518055 China

Background: Flu circulates all over the world. The worldwide infection places a substantial burden on people’s health every year. Regardless of the characteristic of the worldwide circulation of flu, most previous studies focused on regional prediction of flu outbreaks. The methodology of considering the spatio-temporal correlation could help forecast flu outbreaks more precisely. Furthermore, forecasting a long-term flu outbreak, and understanding flu infection trend more accurately could help hospitals, clinics, and pharmaceutical companies to better prepare for annual flu outbreaks. Predicting a sequence of values in future, namely, the multi-step predication of flu outbreaks should cause concern. Therefore, we highlight the importance of developing spatio-temporal methodologies to perform multi-step prediction of worldwide flu outbreaks. Results: We compared the MAPEs of SVM, RF, LSTM models of predicting flu data of the 1-4 week(s) ahead with and without other countries’ flu data. We found the LSTM models achieved the lowest MAPEs in most cases. As for countries in Southern hemisphere, the MAPEs of predicting flu data with other countries are higher than those of predicting without other countries. For countries in Northern hemisphere, the MAPEs of predicting flu data of the 2-4 weeks ahead with other countries are lower than those of predicting without other countries;and the MAPEs of predicting flu data of the 1-weeks ahead with other countries are higher than those of predicting without other countries, except for UK. Conclusions: In this study, we performed the spatio-temporal multi-step prediction of influenza outbreaks. The methodology considering the spatio-temporal features improves the multi-step prediction of flu outbreaks. © 2021, CC BY.

关键词： Forecasting

Backdoor attack against speaker verification

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arXiv 2020年

作者： Zhai, Tongqing Li, Yiming Zhang, Ziqi Wu, Baoyuan Jiang, Yong Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China Secure Computing Lab of Big Data Shenzhen Research Institute of Big Data Shenzhen China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

Speaker verification has been widely and successfully adopted in many mission-critical areas for user identification. The training of speaker verification requires a large amount of data, therefore users usually need to adopt third-party data (e.g., data from the Internet or third-party data company). This raises the question of whether adopting untrusted third-party data can pose a security threat. In this paper, we demonstrate that it is possible to inject the hidden backdoor for infecting speaker verification models by poisoning the training data. Specifically, we design a clustering-based attack scheme where poisoned samples from different clusters will contain different triggers (i.e., pre-defined utterances), based on our understanding of verification tasks. The infected models behave normally on benign samples, while attacker-specified unenrolled triggers will successfully pass the verification even if the attacker has no information about the enrolled speaker. We also demonstrate that existing backdoor attacks cannot be directly adopted in attacking speaker verification. Our approach not only provides a new perspective for designing novel attacks, but also serves as a strong baseline for improving the robustness of verification methods. The code for reproducing main results is available at https://***/zhaitongqing233/Back door-attack-against-speaker-verification. Copyright © 2020, The Authors. All rights reserved.

关键词： Speech recognition

High temperature performance of coaxial h-BN/CNT wires above 1,000 ℃:Thermionic electron emission and thermally activated conductivity

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Nano research 2019年第8期12卷 1855-1861页

作者： Xinhe Yang Peng Liu Duanliang Zhou Feng Gao Xinhe Wang Shiwei Lv Zi Yuan Xiang Jin Wei Zhao Haoming Wei Lina Zhang Jiandong Gao Qunqing Li Shoushan Fan Kaili Jiang State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics and Tsinghua-Foxconn Nanotechnology Research CenterTsinghua UniversityBeijing 100084China Yantai HeFuXiang Ceramics Co. LtdYantai 265205China Fert Beijing Research Institute School of Microelectronics and Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC)Beihang UniversityBeijing 100191China

The development of wires and cables that can tolerate extremely high temperatures will be very important for probing extreme environments, such as in solar exploration, fire disasters, high-temperature materials processing, aeronautics and astronautics. In this paper, a lightweight high-temperature coaxial h-boron nitride (BN)/carbon nanotube (CNT) wire is synthesized by the chemical vapor deposition (CVD) epitaxial growth of h-BN on CNT yarn. The epitaxially grown h-BN acts as both an insulating material and a jacket that protects against oxidation. It has been shown that the thermionic electron emission (1,200 K) and thermally activated conductivity (1,000 K) are two principal mechanisms for insulation failure of h-BN at high temperatures. The thermionic emission of h-BN can provide the work function of h-BN, which ranges from 4.22 to 4.61 eV in the temperature range of 1,306-1,787 K. The change in the resistivity of h-BN with temperature follows the ohmic conduction model of an insulator, and it can provide the “electron activation energy”(the energy from the Fermi level to the conduction band of h-BN), which ranges from 2.79 to 3.08 eV, corresponding to a band gap for h-BN ranging from 5.6 to 6.2 eV. However, since the leakage current is very small, both phenomena have no obvious influence on the signal transmission at the working temperature. This lightweight coaxial h-BN/CNT wire can tolerate 1,200 ℃ in air and can transmit electrical signals as normal. It is hoped that this lightweight high-temperature wire will open up new possibilities for a wide range of applications in extreme high-temperature conditions.

关键词： high temperature wire carbon nanotube (CNT) h-boron nitride (h-BN) work function bandgap

Improved Weak Lensing Photometric Redshift Calibration via StratLearn and Hierarchical Modeling

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arXiv 2024年

作者： Autenrieth, Maximilian Wright, Angus H. Trotta, Roberto van Dyk, David A. Stenning, David C. Joachimi, Benjamin Department of Mathematics Imperial College London 180 Queen’s Gate LondonSW7 2AZ United Kingdom German Centre for Cosmological Lensing Bochum44780 Germany SISSA International School for Advanced Studies Via Bonomea 265 Trieste34136 Italy Department of Physics Imperial College London Blackett Laboratory Prince Consort Rd LondonSW72AZ United Kingdom Italian Research Center on High Performance Computing Big Data and Quantum Computing Italy INFN – National Institute for Nuclear Physics Via Valerio 2 Trieste34127 Italy Department of Statistics and Actuarial Science Simon Fraser University Canada Department of Physics and Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom

Discrepancies between cosmological parameter estimates from cosmic shear surveys and from recent Planck cosmic microwave background measurements challenge the ability of the highly successful ΛCDM model to describe the nature of the Universe. To rule out systematic biases in cosmic shear survey analyses, accurate redshift calibration within tomographic bins is key. In this paper, we improve photo-z calibration via Bayesian hierarchical modeling of full galaxy photo-z conditional densities, by employing StratLearn, a recently developed statistical methodology, which accounts for systematic differences in the distribution of the spectroscopic training/source set and the photometric target set. Using realistic simulations that were designed to resemble the KiDS+VIKING-450 dataset, we show that StratLearn-estimated conditional densities improve the galaxy tomographic bin assignment, and that our StratLearn-Bayesian framework leads to nearly unbiased estimates of the target population means. This leads to a factor of ∼ 2 improvement upon the previously best photo-z calibration method. Our approach delivers a maximum bias per tomographic bin of ∆〈z〉 = 0.0095 ± 0.0089, with an average absolute bias of 0.0052 ± 0.0067 across the five tomographic bins. © 2024, CC BY.

关键词： Galaxies

Effect of respiratory microflorae colonization on short and long-time outcomes of respiratory syncytial virus infection in children: A scoping review

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Pediatric Discovery

Pediatric Discovery 2024年第4期2卷

作者： Lidan Gan Enmei Liu Yu Deng Department of Respiratory Medicine Children's Hospital of Chongqing Medical University Chongqing China Ministry of Education Key Laboratory of Child Development and Disorders Children's Medical Big Data Intelligent Application Center Department of Respiratory Medicine Chongqing China National Clinical Research Center for Child Health and Disorders Chongqing China

Respiratory syncytial virus (RSV) is an essential cause of lower respiratory tract infection in children under 2 years of age, especially under 6 months. In decades, studies have shown that the respiratory tract microflorae with RSV infection were related to disease severity and played a role in the development of recurrent wheezing, but the effect of respiratory microflorae on RSV infection are still underestimated. This study aims to conclude the effect of respiratory microflorae colonization on RSV infectious disease severity and recurrent wheezing and provide suggestions for future research directions from the perspective of respiratory tract florae. We conducted a scoping review. Studies were eligible if they reported on the effect of microflorae on RSV infectious diseases among children. We exacted the following information: title, publication time, first author's country, and article type. We finally included 33 articles in this scoping review. The number of studies rapidly increased since 2013 and the highest number of hospitalizations were reported in children <2 years. More than half (69.70%) were conducted in America and most studies are original studies (57.58%). The Review highlighted that the respiratory microflorae played an important role in RSV infectious disease severity and recurrent wheezing. We found that Streptococcus pneumoniae (***), Haemophilus influenza (HI), Moraxella catarrhalis (***), and Staphylococcus aureus (SA) were the dominant profiles in children with RSV infection. Understanding the respective role of respiratory microflorae on RSV infection and its mechanisms would improve prevention and treatment strategies from the perspective of microflorae.

关键词： childhood asthma disease severity microflorae recurrent wheezing respiratory syncytial virus

SIDE-real: Supernova Ia Dust Extinction with truncated marginal neural ratio estimation applied to real data

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arXiv 2024年

作者： Karchev, Konstantin Grayling, Matthew Boyd, Benjamin M. Trotta, Roberto Mandel, Kaisey S. Weniger, Christoph Via Bonomea 265 TriesteI-34136 Italy Institute of Astronomy Kavli Institute for Cosmology Madingley Road CambridgeCB3 0HA United Kingdom Astrophysics Group Physics Department Blackett Lab Imperial College London Prince Consort Road LondonSW7 2AZ United Kingdom INFN - National Institute for Nuclear Physics Via Valerio 2 TriesteI-34127 Italy Italian Research Center on High-Performance Computing Big Data and Quantum Computing Via Magnanelli 2 BO Casalecchio di RenoI-40033 Italy Statistical Laboratory DPMMS University of Cambridge Wilberforce Road CambridgeCB3 0WB United Kingdom University of Amsterdam Science Park 904 AmsterdamNL-1098 XH Netherlands

We present the first fully simulation-based hierarchical analysis of the light curves of a population of low-redshift type Ia supernovæ (SNæ Ia). Our hardware-accelerated forward model, released in the Python package slicsim, includes stochastic variations of each SN's spectral flux distribution (based on the pre-trained BayeSN model), extinction from dust in the host and in the Milky Way, redshift, and realistic instrumental noise. By utilizing truncated marginal neural ratio estimation (TMNRE), a neural network-enabled simulation-based inference technique, we implicitly marginalize over 4000 latent variables (for a set of ≈100 SNæ Ia) to efficiently infer SN Ia absolute magnitudes and host-galaxy dust properties at the population level while also constraining the parameters of individual objects. Amortization of the inference procedure allows us to obtain coverage guarantees for our results through Bayesian validation and frequentist calibration. Furthermore, we show a detailed comparison to full likelihood-based inference, implemented through Hamiltonian Monte Carlo, on simulated data and then apply TMNRE to the light curves of 86 SNæ Ia from the Carnegie Supernova Project, deriving marginal posteriors in excellent agreement with previous work. Given its ability to accommodate arbitrarily complex extensions to the forward model, e.g. different populations based on host properties, redshift evolution, complicated photometric redshift estimates, selection effects, and non-Ia contamination, without significant modifications to the inference procedure, TMNRE has the potential to become the tool of choice for cosmological parameter inference from future, large SN Ia samples. © 2024, CC BY.

关键词： Supernovae